The Place of Interoception and Theory of Mind in Deception
A Subtitle
Introduction
Packages & Data
Packages
This document was prepared on 2021-11-11.
library(tidyverse)
library(patchwork)
library(glmmTMB)
library(report)
library(parameters)
library(correlation)
library(modelbased)
library(performance)
library(see)
summary(report::report(sessionInfo()))The analysis was done using the R Statistical language (v4.1.1; R Core Team, 2021) on Windows 10 x64, using the packages effectsize (v0.4.5.4), ggplot2 (v3.3.5), stringr (v1.4.0), forcats (v0.5.1), tidyr (v1.1.4), readr (v2.0.2), dplyr (v1.0.7), tibble (v3.1.4), purrr (v0.3.4), parameters (v0.15.0.1), insight (v0.14.5.1), performance (v0.7.3.5), see (v0.7.0.1), easystats (v0.4.3), correlation (v0.7.1.1), modelbased (v0.7.0.1), bayestestR (v0.11.5), report (v0.4.0), datawizard (v0.2.1.9000), glmmTMB (v1.1.2.3), patchwork (v1.1.1) and tidyverse (v1.3.1).
Data
# setwd("C:/Users/user/Desktop/Sputnik/2019-23/DeceptionInteroTom")
df <- read.csv("data/data_combined.csv") %>%
mutate(ID = as.factor(paste0("S", ID)),
condition = as.factor(condition),
item = as.factor(item),
style = as.factor(style),
instruction = as.factor(instruction)) |>
#TODO: This renaming should be done at the preprocessing stage
rename("Participant" = "ID",
"Condition" = "condition",
"Item" = "item",
"Phrasing" = "style",
"Answer" = "instruction",
"YONI_Total" = "yoni_total",
"YONI_Affective" = "yoni_affective",
"YONI_Cognitive" = "yoni_cognitive",
"YONI_Physical" = "yoni_physical",
"BES_Total" = "BES_total",
"BES_Cognitive" = "BES_cognitive",
"BES_Affective" = "BES_affective",
"HCT_Confidence" = "HCT_confidence",
"HCT_Accuracy" = "HCT_accuracy",
"HCT_Awareness" = "HCT_awareness",
"MAIA_Total" = "MAIA_total",
"MAIA_AttentionRegulation" = "MAIA_attention_regulation",
"MAIA_BodyListening" = "MAIA_body_listening",
"MAIA_EmotionalAwareness" = "MAIA_emotional_awareness",
"MAIA_NotDistracting" = "MAIA_not_distracting",
"MAIA_NotWorrying" = "MAIA_not_worrying",
"MAIA_Noticing" = "MAIA_noticing",
"MAIA_SelfRegulation" = "MAIA_self_regulation",
"MAIA_Trusting" = "MAIA_trusting",
"LIE_Ability" = "lie_ability",
"LIE_Frequency" = "lie_frequency",
"LIE_Negativity" = "lie_negativity",
"LIE_Contextuality" = "lie_contextuality",
"Confidence" = "DT_confidence",
"RT" = "DT_RT") |>
mutate(Answer = fct_recode(Answer, Lie = "LIE", Truth = "TRUTH")) |>
select(-HCT_guess, -HCT_noguess, -HCT_onebreath)
cat(paste("The data consists of",
report::report_participants(df,
participants = "Participant",
sex = "Gender",
age = "Age")))The data consists of 30 participants (Mean age = 21.1, SD = 2.1, range: [18, 25]; 63.3% females)
Measures
Theory of Mind / Empathy
Yoni Task
df %>%
group_by(Participant) %>%
select(starts_with("YONI_")) |>
summarise_all(mean, na.rm=TRUE) |>
tidyr::pivot_longer(-Participant, values_to = "Scores") |>
mutate(name = paste0(str_replace(name, "_", " ("), ")")) |>
ggplot(aes(x = Scores, fill = name)) +
geom_density() +
scale_fill_manual(values = c("YONI (Affective)" = "Purple",
"YONI (Cognitive)" = "Blue",
"YONI (Physical)" = "Green",
"YONI (Total)"= "DarkBlue"),
guide = "none") +
facet_wrap(~name, scales = "free")
BES Questionnaire
df %>%
group_by(Participant) %>%
select(starts_with("BES_")) |>
summarise_all(mean, na.rm=TRUE) |>
tidyr::pivot_longer(-Participant, values_to = "Scores") |>
mutate(name = paste0(str_replace(name, "_", " ("), ")")) |>
ggplot(aes(x = Scores, fill = name)) +
geom_density() +
scale_fill_manual(values = c("BES (Affective)" = "Purple",
"BES (Cognitive)" = "Blue",
"BES (Total)"= "DarkBlue"),
guide = "none") +
facet_wrap(~name, scales = "free")
Interoception
Heartbeat Counting Task (HCT)
df %>%
group_by(Participant) %>%
select(starts_with("HCT_")) |>
summarise_all(mean, na.rm=TRUE) |>
tidyr::pivot_longer(-Participant, values_to = "Scores") |>
mutate(name = paste0(str_replace(name, "_", " ("), ")")) |>
ggplot(aes(x = Scores, fill = name)) +
geom_density() +
scale_fill_manual(values = c("HCT (Accuracy)" = "Red",
"HCT (Awareness)" = "Orange",
"HCT (Confidence)"= "DarkOrange"),
guide = "none") +
facet_wrap(~name, scales = "free")> Warning: Removed 6 rows containing non-finite values (stat_density).
MAIA
df %>%
group_by(Participant) %>%
select(starts_with("MAIA_")) |>
summarise_all(mean, na.rm=TRUE) |>
tidyr::pivot_longer(-Participant, values_to = "Scores") |>
mutate(name = paste0(str_replace(name, "_", " ("), ")")) |>
ggplot(aes(x = Scores, fill = name)) +
geom_density() +
scale_fill_brewer(palette = "Reds", guide = "none") +
facet_wrap(~name, scales = "free")
Deception
LIE Scale
df %>%
group_by(Participant) %>%
select(starts_with("LIE_")) |>
summarise_all(mean, na.rm=TRUE) |>
tidyr::pivot_longer(-Participant, values_to = "Scores") |>
mutate(name = paste0(str_replace(name, "_", " ("), ")")) |>
ggplot(aes(x = Scores, fill = name)) +
geom_density() +
scale_fill_manual(values = c("LIE (Ability)" = "#2196F3",
"LIE (Frequency)" = "#4CAF50",
"LIE (Contextuality)"= "#FF9800",
"LIE (Negativity)"= "#E91E63"),
guide = "none") +
facet_wrap(~name, scales = "free")
Deception Task
Summary Table
df |>
group_by(Participant, Answer) |>
summarise(Confidence = paste(insight::format_value(mean(Confidence, na.rm = TRUE)),
" +- ",
insight::format_value(sd(Confidence, na.rm = TRUE))),
RT = paste(insight::format_value(mean(RT, na.rm = TRUE)),
" +- ",
insight::format_value(sd(RT, na.rm = TRUE)))) |>
arrange(Participant) |>
knitr::kable()| Participant | Answer | Confidence | RT |
|---|---|---|---|
| S1 | Lie | 0.40 +- 0.10 | 3.69 +- 0.69 |
| S1 | Truth | 0.52 +- 0.15 | 3.75 +- 0.79 |
| S10 | Lie | 0.59 +- 0.28 | 3.20 +- 0.71 |
| S10 | Truth | 0.84 +- 0.13 | 3.22 +- 0.62 |
| S11 | Lie | 0.38 +- 0.31 | 3.60 +- 0.69 |
| S11 | Truth | 0.72 +- 0.26 | 3.48 +- 0.62 |
| S12 | Lie | 0.58 +- 0.13 | 3.77 +- 1.42 |
| S12 | Truth | 0.64 +- 0.16 | 4.40 +- 1.72 |
| S13 | Lie | 0.28 +- 0.25 | 7.22 +- 0.82 |
| S13 | Truth | 0.85 +- 0.17 | 7.57 +- 0.81 |
| S14 | Lie | 0.52 +- 0.29 | 4.13 +- 0.86 |
| S14 | Truth | 0.62 +- 0.26 | 5.29 +- 1.54 |
| S15 | Lie | +- | 4.38 +- 0.94 |
| S15 | Truth | +- | 4.15 +- 1.04 |
| S16 | Lie | 0.41 +- 0.18 | 4.94 +- 1.13 |
| S16 | Truth | 0.66 +- 0.11 | 5.07 +- 1.01 |
| S17 | Lie | 0.63 +- 0.31 | 2.72 +- 0.63 |
| S17 | Truth | 0.77 +- 0.19 | 2.72 +- 0.51 |
| S18 | Lie | 0.21 +- 0.33 | 5.88 +- 2.54 |
| S18 | Truth | 0.79 +- 0.36 | 5.13 +- 1.98 |
| S19 | Lie | +- | 3.44 +- 1.55 |
| S19 | Truth | +- | 3.71 +- 2.05 |
| S2 | Lie | 0.10 +- 0.17 | 6.46 +- 1.95 |
| S2 | Truth | 0.94 +- 0.08 | 6.83 +- 1.71 |
| S20 | Lie | 0.37 +- 0.32 | 3.79 +- 0.74 |
| S20 | Truth | 0.80 +- 0.17 | 4.04 +- 1.02 |
| S21 | Lie | 0.55 +- 0.12 | 4.97 +- 1.01 |
| S21 | Truth | 0.74 +- 0.18 | 5.09 +- 1.04 |
| S22 | Lie | 0.13 +- 0.29 | 4.78 +- 2.94 |
| S22 | Truth | 0.81 +- 0.36 | 5.12 +- 2.99 |
| S23 | Lie | +- | 3.10 +- 1.18 |
| S23 | Truth | +- | 2.99 +- 0.80 |
| S24 | Lie | 0.27 +- 0.22 | 2.81 +- 0.68 |
| S24 | Truth | 0.72 +- 0.17 | 2.69 +- 0.64 |
| S25 | Lie | 0.63 +- 0.33 | 3.71 +- 1.11 |
| S25 | Truth | 0.85 +- 0.22 | 3.71 +- 0.92 |
| S26 | Lie | 0.46 +- 0.21 | 3.32 +- 0.61 |
| S26 | Truth | 0.70 +- 0.16 | 3.23 +- 0.53 |
| S27 | Lie | 0.32 +- 0.09 | 5.19 +- 1.88 |
| S27 | Truth | 0.68 +- 0.07 | 4.75 +- 1.78 |
| S28 | Lie | 0.49 +- 0.36 | 3.78 +- 0.83 |
| S28 | Truth | 0.59 +- 0.32 | 3.69 +- 0.50 |
| S29 | Lie | 0.58 +- 0.50 | 3.22 +- 0.56 |
| S29 | Truth | 0.90 +- 0.30 | 3.31 +- 0.68 |
| S3 | Lie | +- | 3.35 +- 0.80 |
| S3 | Truth | +- | 3.42 +- 0.96 |
| S30 | Lie | 0.80 +- 0.28 | 4.09 +- 1.10 |
| S30 | Truth | 0.88 +- 0.18 | 3.80 +- 0.99 |
| S4 | Lie | 0.67 +- 0.18 | 3.86 +- 0.89 |
| S4 | Truth | 0.77 +- 0.13 | 3.81 +- 0.80 |
| S5 | Lie | 0.53 +- 0.23 | 3.41 +- 0.63 |
| S5 | Truth | 0.72 +- 0.21 | 3.45 +- 0.69 |
| S6 | Lie | 0.11 +- 0.15 | 3.10 +- 0.52 |
| S6 | Truth | 0.90 +- 0.09 | 3.24 +- 0.64 |
| S7 | Lie | 0.62 +- 0.33 | 3.84 +- 0.58 |
| S7 | Truth | 0.69 +- 0.29 | 3.69 +- 0.55 |
| S8 | Lie | 0.44 +- 0.17 | 4.48 +- 0.95 |
| S8 | Truth | 0.71 +- 0.16 | 4.59 +- 1.12 |
| S9 | Lie | 5.79e-04 +- 9.06e-04 | 4.75 +- 1.42 |
| S9 | Truth | 1.00 +- 1.18e-03 | 5.15 +- 1.88 |
Outliers
df <- df |>
dplyr::filter(Participant != "S9", # Extreme answers
!Participant %in% c("S3", "S15", "S19", "S23")) # No dataDistributions
p1 <- df |>
dplyr::filter(!Participant %in% c("S29")) |>
ggplot(aes(x = Confidence, fill = Participant)) +
geom_density(alpha = 0.1) +
see::scale_fill_material_d(palette = "rainbow", guide = "none") +
see::theme_modern() +
scale_x_continuous(labels = scales::percent, expand=expansion(c(0, .05))) +
scale_y_continuous(expand=expansion(c(0, .05))) +
facet_wrap(~Answer)
p2 <- df |>
dplyr::filter(!Participant %in% c("S29")) |>
ggplot(aes(x = RT, fill = Participant)) +
geom_density(alpha = 0.1) +
see::scale_fill_material_d(palette = "rainbow", guide = "none") +
scale_x_continuous(expand=expansion(c(0, .05))) +
scale_y_continuous(expand=expansion(c(0, .05))) +
facet_wrap(~Answer)
p1 / p2
Manipulation Checks
Correlation
dfsub <- df |>
select(Participant,
starts_with("YONI_"),
starts_with("BES_"),
starts_with("HCT_"),
starts_with("MAIA_"),
starts_with("LIE_")) |>
group_by(Participant) |>
summarise_all(mean)Theory of Mind / Empathy
r <- correlation(select(dfsub, starts_with("YONI_")),
select(dfsub, starts_with("BES_")),
p_adjust = "none")
summary(r) |>
plot()
Interoception
r <- correlation(select(dfsub, starts_with("MAIA_")),
select(dfsub, starts_with("HCT_")),
p_adjust = "none")
summary(r) |>
plot()
ToM and Interoception
r <- correlation(select(dfsub, starts_with(c("MAIA_", "HCT_"))),
select(dfsub, starts_with(c("YONI_", "BES_"))),
p_adjust = "none")
summary(r) |>
plot()
Phrasing
- Main effect of phrasing on RT only. Indirect questions lead to slower answers.
RT
model <- glmmTMB(RT ~ Answer * Phrasing + (1|Participant) + (1|Item), data = df)
parameters::parameters(model, effects = "fixed")> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ------------------------------------------------------------------------------------------
> (Intercept) | 4.01 | 0.24 | [ 3.54, 4.47] | 16.80 | < .001
> Answer [Truth] | 0.03 | 0.07 | [-0.11, 0.17] | 0.39 | 0.694
> Phrasing [Indirect] | 0.33 | 0.07 | [ 0.19, 0.48] | 4.58 | < .001
> Answer [Truth] * Phrasing [Indirect] | 0.08 | 0.10 | [-0.12, 0.28] | 0.79 | 0.429estimate_means(model, at = c("Answer", "Phrasing")) |>
plot(show_data = "none") 
Confidence
model <- glmmTMB(Confidence ~ Answer * Phrasing + (1|Participant) + (1|Item), data = df)
parameters::parameters(model)> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ------------------------------------------------------------------------------------------
> (Intercept) | 0.45 | 0.02 | [ 0.41, 0.49] | 20.53 | < .001
> Answer [Truth] | 0.31 | 0.02 | [ 0.28, 0.34] | 18.53 | < .001
> Phrasing [Indirect] | -0.02 | 0.02 | [-0.05, 0.02] | -0.93 | 0.353
> Answer [Truth] * Phrasing [Indirect] | 2.98e-03 | 0.02 | [-0.04, 0.05] | 0.13 | 0.899
>
> # Random Effects
>
> Parameter | Coefficient | 95% CI
> --------------------------------------------------------
> SD (Intercept: Participant) | 0.09 | [0.07, 0.13]
> SD (Intercept: Item) | 7.89e-03 | [0.07, 0.13]
> SD (Residual) | 0.26 | [0.25, 0.27]estimate_means(model, at = c("Answer", "Phrasing")) |>
plot(show_data = "none") 
Lies and RT
- The faster they answer, the more confident they are in their lies.
# Adjustments for beta models
df$Confidence[df$Confidence == 1] <- 0.99999
df$Confidence[df$Confidence == 0] <- 0.00001
model <- glmmTMB(Confidence ~ RT * Answer + Phrasing + (1|Participant) + (1|Item),
data = df, family = beta_family())
parameters::parameters(model, effects = "fixed")> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> --------------------------------------------------------------------------
> (Intercept) | 0.77 | 0.13 | [ 0.51, 1.04] | 5.80 | < .001
> RT | -0.25 | 0.03 | [-0.30, -0.20] | -9.53 | < .001
> Answer [Truth] | -0.02 | 0.15 | [-0.31, 0.26] | -0.16 | 0.876
> Phrasing [Indirect] | -1.08e-03 | 0.05 | [-0.11, 0.10] | -0.02 | 0.984
> RT * Answer [Truth] | 0.29 | 0.03 | [ 0.23, 0.35] | 9.03 | < .001estimate_relation(model, at = c("RT", "Answer")) |>
plot(length = 50, point = list(alpha = 0.3, size = 3.5)) 
Effect of Condition
Confidence
- Significant interaction between the condition and the answer: the effect of answer (being more confident in truths than in lies) is lower in the social condition.
- Effect mostly driven by the social condition that increases the confidence in lies.
- In other words: harder to lie in the polygraph condition: less obvious feedback cues?
model <- glmmTMB(Confidence ~ Answer * Condition + (1|Participant) + (1|Item),
data = df, family = beta_family())
parameters::parameters(model, effects = "fixed")> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ------------------------------------------------------------------------------------------
> (Intercept) | -0.34 | 0.08 | [-0.51, -0.18] | -4.08 | < .001
> Answer [Truth] | 1.29 | 0.08 | [ 1.14, 1.44] | 16.45 | < .001
> Condition [Social] | 0.14 | 0.08 | [-0.01, 0.29] | 1.78 | 0.075
> Answer [Truth] * Condition [Social] | -0.22 | 0.11 | [-0.43, -0.01] | -2.06 | 0.039estimate_means(model, at = c("Condition", "Answer")) |>
plot(show_data = "none") 
RT
- Significant main effect of the condition: people are slower in the polygraph condition.
- Consistent with the “less intuitive cues.”
model <- glmmTMB(RT ~ Answer * Condition + (1|Participant) + (1|Item),
data = df)
parameters::parameters(model, effects = "fixed")> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ------------------------------------------------------------------------------------------
> (Intercept) | 4.42 | 0.24 | [ 3.96, 4.89] | 18.57 | < .001
> Answer [Truth] | 0.07 | 0.07 | [-0.07, 0.21] | 1.00 | 0.316
> Condition [Social] | -0.51 | 0.07 | [-0.65, -0.37] | -7.05 | < .001
> Answer [Truth] * Condition [Social] | -5.48e-03 | 0.10 | [-0.20, 0.19] | -0.05 | 0.957estimate_means(model, at = c("Condition", "Answer")) |>
plot(show_data = "none") 
Theory of Mind / Empathy
Yoni Task
- When instructed to lie, the decrease in confidence in participants with higher yoni score is less in social than polygraph condition.
Total Score
Confidence
model <- glmmTMB(Confidence ~ Answer / (Condition * YONI_Total) + (1|Participant) + (1|Item),
data = df, family = beta_family())
get_parameters <- function(model) {
# Parameters
params <- parameters::parameters(model, effects = "fixed")
# Marginal effects
at <- c("Answer", "Condition")
trend <- insight::find_predictors(model)$conditional
trend <- trend[!trend %in% at]
marg <- modelbased::estimate_slopes(model, trend = trend, at = at)
# Output
list(params = params, marginal_effects = marg)
}
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ----------------------------------------------------------------------------------------------------
> (Intercept) | 2.70 | 0.95 | [ 0.84, 4.56] | 2.85 | 0.004
> Answer [Truth] | -3.67 | 0.89 | [-5.41, -1.92] | -4.12 | < .001
> Answer [Lie] * ConditionSocial | -2.47 | 0.90 | [-4.23, -0.71] | -2.76 | 0.006
> Answer [Truth] * ConditionSocial | 2.06 | 0.88 | [ 0.35, 3.78] | 2.36 | 0.018
> Answer [Lie] * YONI Total | -0.04 | 0.01 | [-0.06, -0.01] | -3.22 | 0.001
> Answer [Truth] * YONI Total | 0.02 | 0.01 | [ 0.00, 0.04] | 2.04 | 0.041
> Answer [Lie] * ConditionSocial * YONI Total | 0.03 | 0.01 | [ 0.01, 0.05] | 2.91 | 0.004
> Answer [Truth] * ConditionSocial * YONI Total | -0.03 | 0.01 | [-0.05, -0.01] | -2.46 | 0.014results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1980) | p
> --------------------------------------------------------------------------
> Polygraph | Lie | -0.04 | 0.01 | [-0.06, -0.01] | -3.22 | 0.001
> Social | Lie | -5.13e-03 | 0.01 | [-0.03, 0.02] | -0.46 | 0.643
> Polygraph | Truth | 0.02 | 0.01 | [ 0.00, 0.04] | 2.04 | 0.042
> Social | Truth | -2.66e-03 | 0.01 | [-0.02, 0.02] | -0.24 | 0.807
> Marginal effects estimated for YONI_Totalplot_effect <- function(model, results, var = "YONI_Total", outcome = "Confidence") {
data <- df |>
group_by(Participant, Answer, Condition) |>
summarise({{var}} := mean(.data[[var]], na.rm = TRUE),
SD = sd(.data[[outcome]], na.rm = TRUE),
{{outcome}} := mean(.data[[outcome]], na.rm = TRUE),
CI_low = .data[[outcome]] - SD / 2,
CI_high = .data[[outcome]] + SD / 2)
link_data <- estimate_relation(model, at = c("Condition", var, "Answer"), length = 30)
range_x <- diff(range(data[[var]]))
annot <- results$marginal_effects
annot$Label <- insight::format_p(annot$p, stars_only = TRUE)
annot$x <- mean(range(data[[var]]))
annot$x <- ifelse(annot$Condition == "Polygraph",
annot$x - 0.05 * range_x,
annot$x + 0.05 * range_x)
annot$y <- max(data[[outcome]]) - 0.01 * diff(range(data[[outcome]]))
p <- ggplot(link_data, aes_string(x = var, y = "Predicted")) +
geom_pointrange(data = data,
aes_string(y = outcome,
color = "Condition",
ymin = "CI_low",
ymax = "CI_high"),
position = position_dodge(width = 0.02 * range_x)) +
geom_ribbon(aes(ymin = CI_low, ymax = CI_high, fill = Condition), alpha = 0.33) +
geom_line(aes(color = Condition)) +
geom_text(data=annot, aes(x = x, y = y, label = Label, color = Condition),
size = 8, show.legend = FALSE) +
labs(y = ifelse(outcome == "RT", "Reaction Time (s)", "Confidence"),
x = paste0(stringr::str_replace(var, "_", " ("), ")")) +
scale_color_manual(values = c("Polygraph" = "#FF5722", "Social" = "#2196F3")) +
scale_fill_manual(values = c("Polygraph" = "#FF5722", "Social" = "#2196F3")) +
facet_wrap(~Answer)
# Narrow y range
if(outcome == "Confidence") {
p <- p +
coord_cartesian(ylim = c(0, 1)) +
scale_y_continuous(expand=expansion(c(0, 0)))
}
p
}
p_conf_yoni_total <- plot_effect(model, results, var = "YONI_Total", outcome = "Confidence")
# p_conf_yoni_totalRT
model <- glmmTMB(RT ~ Answer / (Condition * YONI_Total) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> --------------------------------------------------------------------------------------------------------
> (Intercept) | 0.60 | 2.53 | [-4.37, 5.57] | 0.24 | 0.813
> Answer [Truth] | 2.81 | 0.81 | [ 1.23, 4.40] | 3.48 | < .001
> Answer [Lie] * ConditionSocial | 1.35 | 0.81 | [-0.24, 2.93] | 1.67 | 0.095
> Answer [Truth] * ConditionSocial | -1.49 | 0.81 | [-3.07, 0.09] | -1.85 | 0.065
> Answer [Lie] * YONI Total | 0.04 | 0.03 | [-0.01, 0.10] | 1.52 | 0.130
> Answer [Truth] * YONI Total | 0.01 | 0.03 | [-0.05, 0.07] | 0.43 | 0.668
> Answer [Lie] * ConditionSocial * YONI Total | -0.02 | 9.42e-03 | [-0.04, 0.00] | -2.30 | 0.021
> Answer [Truth] * ConditionSocial * YONI Total | 0.01 | 9.42e-03 | [-0.01, 0.03] | 1.22 | 0.223results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1989) | p
> -------------------------------------------------------------------------
> Polygraph | Lie | 0.04 | 0.03 | [-0.01, 0.10] | 1.52 | 0.130
> Social | Lie | 0.02 | 0.03 | [-0.03, 0.08] | 0.78 | 0.435
> Polygraph | Truth | 0.01 | 0.03 | [-0.05, 0.07] | 0.43 | 0.668
> Social | Truth | 0.02 | 0.03 | [-0.03, 0.08] | 0.82 | 0.414
> Marginal effects estimated for YONI_Totalp_rt_yoni_total <- plot_effect(model, results, var = "YONI_Total", outcome = "RT")
# p_rt_yoni_totalCognitive Theory of Mind
Confidence
model <- glmmTMB(Confidence ~ Answer / (Condition * YONI_Cognitive) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> --------------------------------------------------------------------------------------------------------
> (Intercept) | 3.32 | 0.82 | [ 1.71, 4.94] | 4.03 | < .001
> Answer [Truth] | -3.68 | 0.77 | [-5.19, -2.18] | -4.80 | < .001
> Answer [Lie] * ConditionSocial | -3.13 | 0.78 | [-4.66, -1.60] | -4.00 | < .001
> Answer [Truth] * ConditionSocial | 1.24 | 0.76 | [-0.24, 2.73] | 1.64 | 0.101
> Answer [Lie] * YONI Cognitive | -0.12 | 0.03 | [-0.17, -0.07] | -4.47 | < .001
> Answer [Truth] * YONI Cognitive | 0.04 | 0.03 | [-0.01, 0.09] | 1.60 | 0.109
> Answer [Lie] * ConditionSocial * YONI Cognitive | 0.10 | 0.03 | [ 0.06, 0.15] | 4.19 | < .001
> Answer [Truth] * ConditionSocial * YONI Cognitive | -0.04 | 0.02 | [-0.09, 0.00] | -1.75 | 0.079results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1980) | p
> ---------------------------------------------------------------------------
> Polygraph | Lie | -0.12 | 0.03 | [-0.17, -0.07] | -4.47 | < .001
> Social | Lie | -0.01 | 0.03 | [-0.07, 0.04] | -0.49 | 0.624
> Polygraph | Truth | 0.04 | 0.03 | [-0.01, 0.09] | 1.60 | 0.109
> Social | Truth | -3.60e-04 | 0.03 | [-0.05, 0.05] | -0.01 | 0.989
> Marginal effects estimated for YONI_Cognitivep_conf_yoni_cognitive <- plot_effect(model, results, var = "YONI_Cognitive", outcome = "Confidence")
# p_conf_yoni_cognitiveRT
model <- glmmTMB(RT ~ Answer / (Condition * YONI_Cognitive) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> --------------------------------------------------------------------------------------------------------
> (Intercept) | 0.42 | 2.24 | [-3.96, 4.80] | 0.19 | 0.851
> Answer [Truth] | 2.39 | 0.72 | [ 0.98, 3.79] | 3.32 | < .001
> Answer [Lie] * ConditionSocial | 1.60 | 0.72 | [ 0.19, 3.01] | 2.22 | 0.026
> Answer [Truth] * ConditionSocial | -1.03 | 0.72 | [-2.44, 0.38] | -1.44 | 0.151
> Answer [Lie] * YONI Cognitive | 0.13 | 0.07 | [-0.01, 0.27] | 1.80 | 0.072
> Answer [Truth] * YONI Cognitive | 0.05 | 0.07 | [-0.09, 0.20] | 0.76 | 0.447
> Answer [Lie] * ConditionSocial * YONI Cognitive | -0.07 | 0.02 | [-0.11, -0.02] | -2.95 | 0.003
> Answer [Truth] * ConditionSocial * YONI Cognitive | 0.02 | 0.02 | [-0.03, 0.06] | 0.73 | 0.468results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1989) | p
> -------------------------------------------------------------------------
> Polygraph | Lie | 0.13 | 0.07 | [-0.01, 0.27] | 1.80 | 0.072
> Social | Lie | 0.06 | 0.07 | [-0.08, 0.20] | 0.85 | 0.393
> Polygraph | Truth | 0.05 | 0.07 | [-0.09, 0.20] | 0.76 | 0.447
> Social | Truth | 0.07 | 0.07 | [-0.07, 0.21] | 0.99 | 0.320
> Marginal effects estimated for YONI_Cognitivep_rt_yoni_cognitive <- plot_effect(model, results, var = "YONI_Cognitive", outcome = "RT")
# p_rt_yoni_cognitiveAffective Theory of Mind
Confidence
model <- glmmTMB(Confidence ~ Answer / (Condition * YONI_Affective) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ------------------------------------------------------------------------------------------------------
> (Intercept) | 0.30 | 1.07 | [-1.80, 2.41] | 0.28 | 0.777
> Answer [Truth] | -0.09 | 1.02 | [-2.10, 1.91] | -0.09 | 0.927
> Answer [Lie] * ConditionSocial | -1.29 | 1.02 | [-3.29, 0.70] | -1.27 | 0.204
> Answer [Truth] * ConditionSocial | 1.40 | 1.02 | [-0.59, 3.39] | 1.38 | 0.168
> Answer [Lie] * YONI Affective | -0.02 | 0.03 | [-0.06, 0.03] | -0.61 | 0.544
> Answer [Truth] * YONI Affective | 0.02 | 0.03 | [-0.03, 0.07] | 0.68 | 0.498
> Answer [Lie] * ConditionSocial * YONI Affective | 0.03 | 0.02 | [-0.01, 0.08] | 1.41 | 0.159
> Answer [Truth] * ConditionSocial * YONI Affective | -0.04 | 0.02 | [-0.08, 0.01] | -1.47 | 0.142results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1980) | p
> -------------------------------------------------------------------------
> Polygraph | Lie | -0.02 | 0.03 | [-0.06, 0.03] | -0.61 | 0.544
> Social | Lie | 0.02 | 0.03 | [-0.03, 0.07] | 0.72 | 0.469
> Polygraph | Truth | 0.02 | 0.03 | [-0.03, 0.07] | 0.68 | 0.498
> Social | Truth | -0.02 | 0.03 | [-0.07, 0.03] | -0.71 | 0.478
> Marginal effects estimated for YONI_Affectivep_conf_yoni_affective <- plot_effect(model, results, var = "YONI_Affective", outcome = "Confidence")
# p_conf_yoni_affectiveRT
model <- glmmTMB(RT ~ Answer / (Condition * YONI_Affective) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ------------------------------------------------------------------------------------------------------
> (Intercept) | 1.12 | 2.88 | [-4.53, 6.77] | 0.39 | 0.698
> Answer [Truth] | 2.06 | 0.92 | [ 0.26, 3.86] | 2.24 | 0.025
> Answer [Lie] * ConditionSocial | 0.27 | 0.92 | [-1.53, 2.07] | 0.30 | 0.766
> Answer [Truth] * ConditionSocial | -1.72 | 0.92 | [-3.52, 0.08] | -1.87 | 0.061
> Answer [Lie] * YONI Affective | 0.08 | 0.07 | [-0.05, 0.21] | 1.15 | 0.250
> Answer [Truth] * YONI Affective | 0.03 | 0.07 | [-0.10, 0.16] | 0.46 | 0.647
> Answer [Lie] * ConditionSocial * YONI Affective | -0.02 | 0.02 | [-0.06, 0.02] | -0.85 | 0.394
> Answer [Truth] * ConditionSocial * YONI Affective | 0.03 | 0.02 | [-0.01, 0.07] | 1.32 | 0.187results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1989) | p
> -------------------------------------------------------------------------
> Polygraph | Lie | 0.08 | 0.07 | [-0.06, 0.21] | 1.15 | 0.250
> Social | Lie | 0.06 | 0.07 | [-0.07, 0.19] | 0.88 | 0.380
> Polygraph | Truth | 0.03 | 0.07 | [-0.10, 0.16] | 0.46 | 0.647
> Social | Truth | 0.06 | 0.07 | [-0.07, 0.19] | 0.88 | 0.380
> Marginal effects estimated for YONI_Affectivep_rt_yoni_affective <- plot_effect(model, results, var = "YONI_Affective", outcome = "RT")
# p_rt_yoni_affectivePhysical Theory of Mind
Confidence
model <- glmmTMB(Confidence ~ Answer / (Condition * YONI_Physical) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ------------------------------------------------------------------------------------------------------
> (Intercept) | 0.87 | 0.40 | [ 0.09, 1.65] | 2.18 | 0.029
> Answer [Truth] | -1.06 | 0.37 | [-1.79, -0.34] | -2.87 | 0.004
> Answer [Lie] * ConditionSocial | -0.42 | 0.37 | [-1.15, 0.30] | -1.14 | 0.253
> Answer [Truth] * ConditionSocial | 0.99 | 0.37 | [ 0.28, 1.71] | 2.71 | 0.007
> Answer [Lie] * YONI Physical | -0.10 | 0.03 | [-0.16, -0.04] | -3.11 | 0.002
> Answer [Truth] * YONI Physical | 0.09 | 0.03 | [ 0.03, 0.16] | 2.94 | 0.003
> Answer [Lie] * ConditionSocial * YONI Physical | 0.05 | 0.03 | [-0.01, 0.10] | 1.52 | 0.128
> Answer [Truth] * ConditionSocial * YONI Physical | -0.09 | 0.03 | [-0.15, -0.03] | -2.98 | 0.003results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1980) | p
> --------------------------------------------------------------------------
> Polygraph | Lie | -0.10 | 0.03 | [-0.16, -0.04] | -3.11 | 0.002
> Social | Lie | -0.05 | 0.03 | [-0.12, 0.01] | -1.70 | 0.090
> Polygraph | Truth | 0.09 | 0.03 | [ 0.03, 0.16] | 2.94 | 0.003
> Social | Truth | 5.67e-03 | 0.03 | [-0.06, 0.07] | 0.18 | 0.859
> Marginal effects estimated for YONI_Physicalp_conf_yoni_physical <- plot_effect(model, results, var = "YONI_Physical", outcome = "Confidence")
# p_conf_yoni_physicalRT
model <- glmmTMB(RT ~ Answer / (Condition * YONI_Physical) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> -------------------------------------------------------------------------------------------------------
> (Intercept) | 3.56 | 1.08 | [ 1.44, 5.69] | 3.28 | 0.001
> Answer [Truth] | 1.18 | 0.34 | [ 0.51, 1.84] | 3.46 | < .001
> Answer [Lie] * ConditionSocial | 0.20 | 0.34 | [-0.46, 0.87] | 0.60 | 0.551
> Answer [Truth] * ConditionSocial | -0.85 | 0.34 | [-1.51, -0.18] | -2.49 | 0.013
> Answer [Lie] * YONI Physical | 0.07 | 0.09 | [-0.10, 0.24] | 0.82 | 0.414
> Answer [Truth] * YONI Physical | -0.02 | 0.09 | [-0.19, 0.15] | -0.23 | 0.820
> Answer [Lie] * ConditionSocial * YONI Physical | -0.06 | 0.03 | [-0.11, 0.00] | -2.13 | 0.033
> Answer [Truth] * ConditionSocial * YONI Physical | 0.03 | 0.03 | [-0.03, 0.08] | 1.01 | 0.312results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1989) | p
> -------------------------------------------------------------------------
> Polygraph | Lie | 0.07 | 0.09 | [-0.10, 0.24] | 0.82 | 0.414
> Social | Lie | 0.01 | 0.09 | [-0.16, 0.18] | 0.15 | 0.883
> Polygraph | Truth | -0.02 | 0.09 | [-0.19, 0.15] | -0.23 | 0.820
> Social | Truth | 7.82e-03 | 0.09 | [-0.16, 0.18] | 0.09 | 0.929
> Marginal effects estimated for YONI_Physicalp_rt_yoni_physical <- plot_effect(model, results, var = "YONI_Physical", outcome = "RT")
# p_rt_yoni_physicalCorrelation with LIE Scale
get_correlation <- function(var = "YONI_", var2 = "LIE_") {
r <- correlation(select(dfsub, starts_with(var2)),
select(dfsub, starts_with(var)),
p_adjust = "none") |>
mutate(Parameter1 = paste0(str_replace(Parameter1, "_", " ("), ")"),
Parameter2 = paste0(str_replace(Parameter2, "_", " ("), ")"))
p <- summary(r) |>
plot() +
theme_minimal()
list(r = r, plot = p)
}
r <- get_correlation(var = "YONI_")
r$plot
Summary
p_conf_yoni_total /
p_conf_yoni_cognitive /
p_conf_yoni_affective /
p_conf_yoni_physical +
patchwork::plot_annotation(title = "Theory of Mind", theme = theme(plot.title = element_text(hjust = 0.5)))
p_rt_yoni_total /
p_rt_yoni_cognitive /
p_rt_yoni_affective /
p_rt_yoni_physical +
patchwork::plot_annotation(title = "Theory of Mind", theme = theme(plot.title = element_text(hjust = 0.5)))
BES
Total Score
Confidence
model <- glmmTMB(Confidence ~ Answer / (Condition * BES_Total) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> -------------------------------------------------------------------------------------------------------
> (Intercept) | 2.29 | 0.74 | [ 0.84, 3.73] | 3.10 | 0.002
> Answer [Truth] | -3.03 | 0.66 | [-4.31, -1.74] | -4.62 | < .001
> Answer [Lie] * ConditionSocial | -1.58 | 0.67 | [-2.91, -0.26] | -2.35 | 0.019
> Answer [Truth] * ConditionSocial | 1.00 | 0.67 | [-0.30, 2.31] | 1.51 | 0.131
> Answer [Lie] * BES Total | -0.03 | 9.77e-03 | [-0.05, -0.02] | -3.57 | < .001
> Answer [Truth] * BES Total | 0.02 | 9.86e-03 | [ 0.00, 0.04] | 2.28 | 0.022
> Answer [Lie] * ConditionSocial * BES Total | 0.02 | 8.92e-03 | [ 0.01, 0.04] | 2.54 | 0.011
> Answer [Truth] * ConditionSocial * BES Total | -0.01 | 8.81e-03 | [-0.03, 0.00] | -1.63 | 0.102results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1980) | p
> -------------------------------------------------------------------------------
> Polygraph | Lie | -0.03 | 9.77e-03 | [-0.05, -0.02] | -3.57 | < .001
> Social | Lie | -0.01 | 0.01 | [-0.03, 0.01] | -1.21 | 0.225
> Polygraph | Truth | 0.02 | 9.86e-03 | [ 0.00, 0.04] | 2.28 | 0.023
> Social | Truth | 8.11e-03 | 9.89e-03 | [-0.01, 0.03] | 0.82 | 0.413
> Marginal effects estimated for BES_Totalp_conf_bes_total <- plot_effect(model, results, var = "BES_Total", outcome = "Confidence")
# p_conf_bes_totalRT
model <- glmmTMB(RT ~ Answer / (Condition * BES_Total) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ------------------------------------------------------------------------------------------------------
> (Intercept) | 4.80 | 2.08 | [ 0.73, 8.87] | 2.31 | 0.021
> Answer [Truth] | 1.96 | 0.66 | [ 0.66, 3.25] | 2.97 | 0.003
> Answer [Lie] * ConditionSocial | 1.27 | 0.66 | [-0.02, 2.56] | 1.93 | 0.054
> Answer [Truth] * ConditionSocial | -1.16 | 0.66 | [-2.45, 0.13] | -1.76 | 0.078
> Answer [Lie] * BES Total | -5.05e-03 | 0.03 | [-0.06, 0.05] | -0.18 | 0.855
> Answer [Truth] * BES Total | -0.03 | 0.03 | [-0.08, 0.02] | -1.10 | 0.273
> Answer [Lie] * ConditionSocial * BES Total | -0.02 | 8.76e-03 | [-0.04, -0.01] | -2.72 | 0.007
> Answer [Truth] * ConditionSocial * BES Total | 8.68e-03 | 8.76e-03 | [-0.01, 0.03] | 0.99 | 0.322results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1989) | p
> -------------------------------------------------------------------------
> Polygraph | Lie | -5.05e-03 | 0.03 | [-0.06, 0.05] | -0.18 | 0.855
> Social | Lie | -0.03 | 0.03 | [-0.08, 0.03] | -1.05 | 0.296
> Polygraph | Truth | -0.03 | 0.03 | [-0.08, 0.02] | -1.10 | 0.273
> Social | Truth | -0.02 | 0.03 | [-0.08, 0.03] | -0.78 | 0.435
> Marginal effects estimated for BES_Totalp_rt_bes_total <- plot_effect(model, results, var = "BES_Total", outcome = "RT")
# p_rt_bes_totalCognitive Empathy
Confidence
model <- glmmTMB(Confidence ~ Answer / (Condition * BES_Cognitive) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> -----------------------------------------------------------------------------------------------------
> (Intercept) | 0.72 | 0.65 | [-0.55, 1.99] | 1.11 | 0.266
> Answer [Truth] | -0.36 | 0.58 | [-1.51, 0.78] | -0.62 | 0.533
> Answer [Lie] * ConditionSocial | -0.46 | 0.60 | [-1.63, 0.72] | -0.76 | 0.446
> Answer [Truth] * ConditionSocial | -0.31 | 0.58 | [-1.46, 0.83] | -0.54 | 0.591
> Answer [Lie] * BES Cognitive | -0.03 | 0.02 | [-0.07, 0.01] | -1.65 | 0.099
> Answer [Truth] * BES Cognitive | 0.02 | 0.02 | [-0.02, 0.05] | 0.92 | 0.358
> Answer [Lie] * ConditionSocial * BES Cognitive | 0.02 | 0.02 | [-0.02, 0.05] | 0.99 | 0.323
> Answer [Truth] * ConditionSocial * BES Cognitive | 6.44e-03 | 0.02 | [-0.03, 0.04] | 0.39 | 0.694results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1980) | p
> -------------------------------------------------------------------------
> Polygraph | Lie | -0.03 | 0.02 | [-0.07, 0.01] | -1.65 | 0.099
> Social | Lie | -0.01 | 0.02 | [-0.05, 0.02] | -0.73 | 0.466
> Polygraph | Truth | 0.02 | 0.02 | [-0.02, 0.05] | 0.92 | 0.358
> Social | Truth | 0.02 | 0.02 | [-0.01, 0.06] | 1.27 | 0.206
> Marginal effects estimated for BES_Cognitivep_conf_bes_cognitive <- plot_effect(model, results, var = "BES_Cognitive", outcome = "Confidence")
# p_conf_bes_cognitiveRT
model <- glmmTMB(RT ~ Answer / (Condition * BES_Cognitive) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> -------------------------------------------------------------------------------------------------------
> (Intercept) | 5.43 | 1.75 | [ 2.00, 8.86] | 3.10 | 0.002
> Answer [Truth] | 1.13 | 0.57 | [ 0.02, 2.24] | 2.00 | 0.046
> Answer [Lie] * ConditionSocial | 2.01 | 0.57 | [ 0.90, 3.12] | 3.54 | < .001
> Answer [Truth] * ConditionSocial | 0.25 | 0.57 | [-0.87, 1.36] | 0.43 | 0.665
> Answer [Lie] * BES Cognitive | -0.03 | 0.05 | [-0.12, 0.07] | -0.58 | 0.562
> Answer [Truth] * BES Cognitive | -0.06 | 0.05 | [-0.15, 0.04] | -1.19 | 0.234
> Answer [Lie] * ConditionSocial * BES Cognitive | -0.07 | 0.02 | [-0.10, -0.04] | -4.47 | < .001
> Answer [Truth] * ConditionSocial * BES Cognitive | -0.02 | 0.02 | [-0.05, 0.01] | -1.35 | 0.178results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1989) | p
> --------------------------------------------------------------------------
> Polygraph | Lie | -0.03 | 0.05 | [-0.12, 0.07] | -0.58 | 0.562
> Social | Lie | -0.10 | 0.05 | [-0.20, 0.00] | -2.03 | 0.043
> Polygraph | Truth | -0.06 | 0.05 | [-0.15, 0.04] | -1.19 | 0.234
> Social | Truth | -0.08 | 0.05 | [-0.18, 0.02] | -1.63 | 0.104
> Marginal effects estimated for BES_Cognitivep_rt_bes_cognitive <- plot_effect(model, results, var = "BES_Cognitive", outcome = "RT")
# p_rt_bes_cognitiveAffective Empathy
Confidence
model <- glmmTMB(Confidence ~ Answer / (Condition * BES_Affective) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> -------------------------------------------------------------------------------------------------------
> (Intercept) | 1.99 | 0.58 | [ 0.86, 3.12] | 3.46 | < .001
> Answer [Truth] | -2.60 | 0.52 | [-3.62, -1.59] | -5.03 | < .001
> Answer [Lie] * ConditionSocial | -1.50 | 0.53 | [-2.53, -0.46] | -2.84 | 0.005
> Answer [Truth] * ConditionSocial | 1.36 | 0.52 | [ 0.33, 2.38] | 2.59 | 0.010
> Answer [Lie] * BES Affective | -0.06 | 0.01 | [-0.09, -0.03] | -4.09 | < .001
> Answer [Truth] * BES Affective | 0.04 | 0.01 | [ 0.01, 0.07] | 2.70 | 0.007
> Answer [Lie] * ConditionSocial * BES Affective | 0.04 | 0.01 | [ 0.01, 0.07] | 3.10 | 0.002
> Answer [Truth] * ConditionSocial * BES Affective | -0.04 | 0.01 | [-0.06, -0.01] | -2.75 | 0.006results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1980) | p
> ---------------------------------------------------------------------------
> Polygraph | Lie | -0.06 | 0.01 | [-0.09, -0.03] | -4.09 | < .001
> Social | Lie | -0.02 | 0.01 | [-0.05, 0.01] | -1.21 | 0.225
> Polygraph | Truth | 0.04 | 0.01 | [ 0.01, 0.07] | 2.70 | 0.007
> Social | Truth | 3.24e-03 | 0.01 | [-0.03, 0.03] | 0.22 | 0.823
> Marginal effects estimated for BES_Affectivep_conf_bes_affective <- plot_effect(model, results, var = "BES_Affective", outcome = "Confidence")
# p_conf_bes_affectiveRT
model <- glmmTMB(RT ~ Answer / (Condition * BES_Affective) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> -------------------------------------------------------------------------------------------------------
> (Intercept) | 4.13 | 1.63 | [ 0.93, 7.33] | 2.53 | 0.012
> Answer [Truth] | 1.44 | 0.51 | [ 0.44, 2.45] | 2.82 | 0.005
> Answer [Lie] * ConditionSocial | -0.30 | 0.51 | [-1.31, 0.70] | -0.59 | 0.554
> Answer [Truth] * ConditionSocial | -1.80 | 0.51 | [-2.80, -0.79] | -3.51 | < .001
> Answer [Lie] * BES Affective | 7.53e-03 | 0.04 | [-0.07, 0.09] | 0.18 | 0.854
> Answer [Truth] * BES Affective | -0.03 | 0.04 | [-0.11, 0.05] | -0.67 | 0.506
> Answer [Lie] * ConditionSocial * BES Affective | -5.15e-03 | 0.01 | [-0.03, 0.02] | -0.40 | 0.688
> Answer [Truth] * ConditionSocial * BES Affective | 0.03 | 0.01 | [ 0.01, 0.06] | 2.53 | 0.011results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1989) | p
> -------------------------------------------------------------------------
> Polygraph | Lie | 7.53e-03 | 0.04 | [-0.07, 0.09] | 0.18 | 0.854
> Social | Lie | 2.38e-03 | 0.04 | [-0.08, 0.08] | 0.06 | 0.954
> Polygraph | Truth | -0.03 | 0.04 | [-0.11, 0.05] | -0.67 | 0.506
> Social | Truth | 5.28e-03 | 0.04 | [-0.07, 0.09] | 0.13 | 0.897
> Marginal effects estimated for BES_Affectivep_rt_bes_affective <- plot_effect(model, results, var = "BES_Affective", outcome = "RT")
# p_rt_bes_affectiveCorrelation with LIE Scale
r <- get_correlation(var = "BES_")
r$plot
Summary
p_conf_bes_total /
p_conf_bes_cognitive /
p_conf_bes_affective +
patchwork::plot_annotation(title = "Empathy", theme = theme(plot.title = element_text(hjust = 0.5)))
p_rt_bes_total /
p_rt_bes_cognitive /
p_rt_bes_affective +
patchwork::plot_annotation(title = "Empathy", theme = theme(plot.title = element_text(hjust = 0.5)))
Interoception
Heartbeat Counting
Accuracy
Confidence
model <- glmmTMB(Confidence ~ Answer / (Condition * HCT_Accuracy) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ------------------------------------------------------------------------------------------------------
> (Intercept) | -1.17 | 0.26 | [-1.68, -0.67] | -4.57 | < .001
> Answer [Truth] | 2.76 | 0.23 | [ 2.30, 3.22] | 11.86 | < .001
> Answer [Lie] * ConditionSocial | 0.62 | 0.23 | [ 0.16, 1.08] | 2.67 | 0.008
> Answer [Truth] * ConditionSocial | -0.59 | 0.23 | [-1.04, -0.14] | -2.56 | 0.010
> Answer [Lie] * HCT Accuracy | 1.37 | 0.40 | [ 0.59, 2.16] | 3.44 | < .001
> Answer [Truth] * HCT Accuracy | -1.06 | 0.40 | [-1.84, -0.28] | -2.68 | 0.007
> Answer [Lie] * ConditionSocial * HCT Accuracy | -0.81 | 0.36 | [-1.52, -0.09] | -2.21 | 0.027
> Answer [Truth] * ConditionSocial * HCT Accuracy | 0.85 | 0.36 | [ 0.15, 1.55] | 2.37 | 0.018results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1980) | p
> ---------------------------------------------------------------------------
> Polygraph | Lie | 1.37 | 0.40 | [ 0.59, 2.16] | 3.44 | < .001
> Social | Lie | 0.57 | 0.40 | [-0.22, 1.35] | 1.42 | 0.155
> Polygraph | Truth | -1.06 | 0.40 | [-1.84, -0.28] | -2.68 | 0.008
> Social | Truth | -0.21 | 0.40 | [-0.99, 0.57] | -0.53 | 0.595
> Marginal effects estimated for HCT_Accuracyp_conf_hct_accuracy <- plot_effect(model, results, var = "HCT_Accuracy", outcome = "Confidence")RT
model <- glmmTMB(RT ~ Answer / (Condition * HCT_Accuracy) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ----------------------------------------------------------------------------------------------------------
> (Intercept) | 4.87 | 0.70 | [ 3.49, 6.25] | 6.92 | < .001
> Answer [Truth] | -0.30 | 0.22 | [-0.74, 0.13] | -1.37 | 0.170
> Answer [Lie] * ConditionSocial | -0.51 | 0.22 | [-0.94, -0.07] | -2.28 | 0.022
> Answer [Truth] * ConditionSocial | -0.30 | 0.22 | [-0.73, 0.13] | -1.36 | 0.175
> Answer [Lie] * HCT Accuracy | -0.74 | 1.09 | [-2.88, 1.41] | -0.67 | 0.501
> Answer [Truth] * HCT Accuracy | -0.12 | 1.09 | [-2.26, 2.02] | -0.11 | 0.915
> Answer [Lie] * ConditionSocial * HCT Accuracy | -1.95e-03 | 0.34 | [-0.68, 0.67] | -5.65e-03 | 0.995
> Answer [Truth] * ConditionSocial * HCT Accuracy | -0.35 | 0.34 | [-1.02, 0.33] | -1.01 | 0.312results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1989) | p
> -------------------------------------------------------------------------
> Polygraph | Lie | -0.74 | 1.09 | [-2.88, 1.41] | -0.67 | 0.501
> Social | Lie | -0.74 | 1.09 | [-2.88, 1.40] | -0.68 | 0.500
> Polygraph | Truth | -0.12 | 1.09 | [-2.26, 2.03] | -0.11 | 0.915
> Social | Truth | -0.47 | 1.09 | [-2.61, 1.68] | -0.43 | 0.670
> Marginal effects estimated for HCT_Accuracyp_rt_hct_accuracy <- plot_effect(model, results, var = "HCT_Accuracy", outcome = "RT")Interoceptive Confidence
Confidence
model <- glmmTMB(Confidence ~ Answer / (Condition * HCT_Confidence) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> --------------------------------------------------------------------------------------------------------
> (Intercept) | -0.90 | 0.17 | [-1.23, -0.58] | -5.39 | < .001
> Answer [Truth] | 1.88 | 0.16 | [ 1.57, 2.20] | 11.60 | < .001
> Answer [Lie] * ConditionSocial | 0.45 | 0.16 | [ 0.13, 0.77] | 2.75 | 0.006
> Answer [Truth] * ConditionSocial | -0.40 | 0.16 | [-0.71, -0.08] | -2.48 | 0.013
> Answer [Lie] * HCT Confidence | 1.11 | 0.29 | [ 0.54, 1.69] | 3.80 | < .001
> Answer [Truth] * HCT Confidence | -0.07 | 0.29 | [-0.64, 0.50] | -0.25 | 0.804
> Answer [Lie] * ConditionSocial * HCT Confidence | -0.63 | 0.29 | [-1.19, -0.06] | -2.17 | 0.030
> Answer [Truth] * ConditionSocial * HCT Confidence | 0.63 | 0.28 | [ 0.08, 1.19] | 2.24 | 0.025results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1980) | p
> --------------------------------------------------------------------------
> Polygraph | Lie | 1.11 | 0.29 | [ 0.54, 1.69] | 3.80 | < .001
> Social | Lie | 0.49 | 0.30 | [-0.10, 1.07] | 1.64 | 0.101
> Polygraph | Truth | -0.07 | 0.29 | [-0.64, 0.50] | -0.25 | 0.804
> Social | Truth | 0.56 | 0.29 | [-0.02, 1.14] | 1.91 | 0.056
> Marginal effects estimated for HCT_Confidencep_conf_hct_confidence <- plot_effect(model, results, var = "HCT_Confidence", outcome = "Confidence")RT
model <- glmmTMB(RT ~ Answer / (Condition * HCT_Confidence) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> --------------------------------------------------------------------------------------------------------
> (Intercept) | 5.41 | 0.45 | [ 4.53, 6.28] | 12.08 | < .001
> Answer [Truth] | 0.03 | 0.15 | [-0.28, 0.33] | 0.17 | 0.868
> Answer [Lie] * ConditionSocial | -0.71 | 0.15 | [-1.01, -0.41] | -4.64 | < .001
> Answer [Truth] * ConditionSocial | -0.47 | 0.15 | [-0.77, -0.17] | -3.08 | 0.002
> Answer [Lie] * HCT Confidence | -1.96 | 0.78 | [-3.49, -0.43] | -2.51 | 0.012
> Answer [Truth] * HCT Confidence | -1.86 | 0.78 | [-3.40, -0.33] | -2.39 | 0.017
> Answer [Lie] * ConditionSocial * HCT Confidence | 0.41 | 0.27 | [-0.12, 0.94] | 1.52 | 0.129
> Answer [Truth] * ConditionSocial * HCT Confidence | -0.08 | 0.27 | [-0.61, 0.45] | -0.29 | 0.769results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1989) | p
> --------------------------------------------------------------------------
> Polygraph | Lie | -1.96 | 0.78 | [-3.49, -0.43] | -2.51 | 0.012
> Social | Lie | -1.55 | 0.78 | [-3.08, -0.01] | -1.98 | 0.048
> Polygraph | Truth | -1.86 | 0.78 | [-3.40, -0.33] | -2.39 | 0.017
> Social | Truth | -1.94 | 0.78 | [-3.48, -0.41] | -2.49 | 0.013
> Marginal effects estimated for HCT_Confidencep_rt_hct_confidence <- plot_effect(model, results, var = "HCT_Confidence", outcome = "RT")Interoceptive Awareness
Confidence
model <- glmmTMB(Confidence ~ Answer / (Condition * HCT_Awareness) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> -------------------------------------------------------------------------------------------------------
> (Intercept) | -0.35 | 0.08 | [-0.52, -0.19] | -4.15 | < .001
> Answer [Truth] | 1.31 | 0.08 | [ 1.16, 1.46] | 16.82 | < .001
> Answer [Lie] * ConditionSocial | 0.14 | 0.08 | [-0.01, 0.29] | 1.83 | 0.067
> Answer [Truth] * ConditionSocial | -0.09 | 0.08 | [-0.24, 0.06] | -1.14 | 0.254
> Answer [Lie] * HCT Awareness | -0.60 | 0.14 | [-0.87, -0.33] | -4.35 | < .001
> Answer [Truth] * HCT Awareness | 0.19 | 0.14 | [-0.08, 0.46] | 1.40 | 0.161
> Answer [Lie] * ConditionSocial * HCT Awareness | 0.58 | 0.13 | [ 0.33, 0.83] | 4.52 | < .001
> Answer [Truth] * ConditionSocial * HCT Awareness | -3.67e-03 | 0.13 | [-0.25, 0.24] | -0.03 | 0.977results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1980) | p
> ---------------------------------------------------------------------------
> Polygraph | Lie | -0.60 | 0.14 | [-0.87, -0.33] | -4.35 | < .001
> Social | Lie | -0.03 | 0.14 | [-0.30, 0.25] | -0.19 | 0.846
> Polygraph | Truth | 0.19 | 0.14 | [-0.08, 0.46] | 1.40 | 0.161
> Social | Truth | 0.19 | 0.14 | [-0.08, 0.46] | 1.37 | 0.171
> Marginal effects estimated for HCT_Awarenessp_conf_hct_awareness <- plot_effect(model, results, var = "HCT_Awareness", outcome = "Confidence")RT
model <- glmmTMB(RT ~ Answer / (Condition * HCT_Awareness) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> -------------------------------------------------------------------------------------------------------
> (Intercept) | 4.45 | 0.22 | [ 4.01, 4.88] | 19.96 | < .001
> Answer [Truth] | 0.07 | 0.07 | [-0.07, 0.21] | 0.93 | 0.350
> Answer [Lie] * ConditionSocial | -0.51 | 0.07 | [-0.65, -0.37] | -7.16 | < .001
> Answer [Truth] * ConditionSocial | -0.51 | 0.07 | [-0.65, -0.37] | -7.16 | < .001
> Answer [Lie] * HCT Awareness | 0.92 | 0.35 | [ 0.22, 1.61] | 2.58 | 0.010
> Answer [Truth] * HCT Awareness | 0.70 | 0.35 | [ 0.01, 1.40] | 1.98 | 0.047
> Answer [Lie] * ConditionSocial * HCT Awareness | -0.32 | 0.12 | [-0.55, -0.08] | -2.66 | 0.008
> Answer [Truth] * ConditionSocial * HCT Awareness | -0.08 | 0.12 | [-0.31, 0.16] | -0.64 | 0.524results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1989) | p
> -------------------------------------------------------------------------
> Polygraph | Lie | 0.92 | 0.35 | [ 0.22, 1.61] | 2.58 | 0.010
> Social | Lie | 0.60 | 0.35 | [-0.10, 1.29] | 1.68 | 0.093
> Polygraph | Truth | 0.70 | 0.35 | [ 0.01, 1.40] | 1.98 | 0.047
> Social | Truth | 0.63 | 0.35 | [-0.07, 1.32] | 1.77 | 0.077
> Marginal effects estimated for HCT_Awarenessp_rt_hct_awareness <- plot_effect(model, results, var = "HCT_Awareness", outcome = "RT")Correlation with LIE Scale
r <- get_correlation(var = "HCT_")
r$plot
# plot(correlation::cor_test(dfsub, "HCT_Accuracy", "LIE_Ability"))Summary
p_conf_hct_accuracy /
p_conf_hct_confidence /
p_conf_hct_awareness +
patchwork::plot_annotation(title = "Interoception", theme = theme(plot.title = element_text(hjust = 0.5)))
p_rt_hct_accuracy /
p_rt_hct_confidence /
p_rt_hct_awareness +
patchwork::plot_annotation(title = "Interoception", theme = theme(plot.title = element_text(hjust = 0.5)))
MAIA
Total Score
Confidence
model <- glmmTMB(Confidence ~ Answer / (Condition * MAIA_Total) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ----------------------------------------------------------------------------------------------------
> (Intercept) | -2.39 | 0.40 | [-3.17, -1.60] | -5.97 | < .001
> Answer [Truth] | 3.93 | 0.37 | [ 3.20, 4.66] | 10.50 | < .001
> Answer [Lie] * ConditionSocial | 1.48 | 0.38 | [ 0.73, 2.23] | 3.85 | < .001
> Answer [Truth] * ConditionSocial | -0.93 | 0.38 | [-1.67, -0.19] | -2.45 | 0.014
> Answer [Lie] * MAIA Total | 0.75 | 0.14 | [ 0.47, 1.04] | 5.23 | < .001
> Answer [Truth] * MAIA Total | -0.22 | 0.14 | [-0.50, 0.06] | -1.54 | 0.124
> Answer [Lie] * ConditionSocial * MAIA Total | -0.50 | 0.14 | [-0.77, -0.23] | -3.60 | < .001
> Answer [Truth] * ConditionSocial * MAIA Total | 0.31 | 0.14 | [ 0.05, 0.58] | 2.30 | 0.022results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1980) | p
> --------------------------------------------------------------------------
> Polygraph | Lie | 0.75 | 0.14 | [ 0.47, 1.04] | 5.23 | < .001
> Social | Lie | 0.26 | 0.15 | [-0.03, 0.54] | 1.76 | 0.079
> Polygraph | Truth | -0.22 | 0.14 | [-0.50, 0.06] | -1.54 | 0.124
> Social | Truth | 0.09 | 0.15 | [-0.19, 0.38] | 0.64 | 0.521
> Marginal effects estimated for MAIA_Totalp_conf_maia_total <- plot_effect(model, results, var = "MAIA_Total", outcome = "Confidence")RT
model <- glmmTMB(RT ~ Answer / (Condition * MAIA_Total) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ----------------------------------------------------------------------------------------------------
> (Intercept) | 7.47 | 1.07 | [ 5.38, 9.56] | 6.99 | < .001
> Answer [Truth] | -0.19 | 0.36 | [-0.90, 0.52] | -0.53 | 0.596
> Answer [Lie] * ConditionSocial | -2.21 | 0.36 | [-2.92, -1.50] | -6.12 | < .001
> Answer [Truth] * ConditionSocial | -1.65 | 0.36 | [-2.36, -0.94] | -4.57 | < .001
> Answer [Lie] * MAIA Total | -1.12 | 0.38 | [-1.87, -0.37] | -2.91 | 0.004
> Answer [Truth] * MAIA Total | -1.02 | 0.38 | [-1.78, -0.27] | -2.66 | 0.008
> Answer [Lie] * ConditionSocial * MAIA Total | 0.63 | 0.13 | [ 0.37, 0.88] | 4.82 | < .001
> Answer [Truth] * ConditionSocial * MAIA Total | 0.42 | 0.13 | [ 0.16, 0.67] | 3.22 | 0.001results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1989) | p
> --------------------------------------------------------------------------
> Polygraph | Lie | -1.12 | 0.38 | [-1.87, -0.37] | -2.91 | 0.004
> Social | Lie | -0.49 | 0.38 | [-1.25, 0.26] | -1.28 | 0.200
> Polygraph | Truth | -1.02 | 0.38 | [-1.78, -0.27] | -2.66 | 0.008
> Social | Truth | -0.60 | 0.38 | [-1.36, 0.15] | -1.57 | 0.116
> Marginal effects estimated for MAIA_Totalp_rt_maia_total <- plot_effect(model, results, var = "MAIA_Total", outcome = "RT")Noticing
Confidence
model <- glmmTMB(Confidence ~ Answer / (Condition * MAIA_Noticing) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> -------------------------------------------------------------------------------------------------------
> (Intercept) | -1.64 | 0.37 | [-2.37, -0.92] | -4.45 | < .001
> Answer [Truth] | 3.44 | 0.32 | [ 2.81, 4.08] | 10.69 | < .001
> Answer [Lie] * ConditionSocial | 1.70 | 0.34 | [ 1.03, 2.36] | 5.03 | < .001
> Answer [Truth] * ConditionSocial | -0.57 | 0.33 | [-1.22, 0.08] | -1.71 | 0.087
> Answer [Lie] * MAIA Noticing | 0.39 | 0.11 | [ 0.18, 0.60] | 3.65 | < .001
> Answer [Truth] * MAIA Noticing | -0.25 | 0.11 | [-0.47, -0.04] | -2.34 | 0.019
> Answer [Lie] * ConditionSocial * MAIA Noticing | -0.47 | 0.10 | [-0.66, -0.28] | -4.80 | < .001
> Answer [Truth] * ConditionSocial * MAIA Noticing | 0.15 | 0.10 | [-0.04, 0.33] | 1.52 | 0.130results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1980) | p
> ---------------------------------------------------------------------------
> Polygraph | Lie | 0.39 | 0.11 | [ 0.18, 0.60] | 3.65 | < .001
> Social | Lie | -0.08 | 0.11 | [-0.30, 0.14] | -0.70 | 0.485
> Polygraph | Truth | -0.25 | 0.11 | [-0.47, -0.04] | -2.34 | 0.019
> Social | Truth | -0.11 | 0.11 | [-0.32, 0.11] | -0.99 | 0.325
> Marginal effects estimated for MAIA_Noticingp_conf_maia_noticing <- plot_effect(model, results, var = "MAIA_Noticing", outcome = "Confidence")RT
model <- glmmTMB(RT ~ Answer / (Condition * MAIA_Noticing) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> -------------------------------------------------------------------------------------------------------
> (Intercept) | 6.72 | 1.03 | [ 4.70, 8.73] | 6.52 | < .001
> Answer [Truth] | -0.98 | 0.33 | [-1.64, -0.33] | -2.96 | 0.003
> Answer [Lie] * ConditionSocial | -1.92 | 0.33 | [-2.57, -1.26] | -5.76 | < .001
> Answer [Truth] * ConditionSocial | -0.75 | 0.33 | [-1.40, -0.10] | -2.25 | 0.024
> Answer [Lie] * MAIA Noticing | -0.68 | 0.30 | [-1.26, -0.10] | -2.28 | 0.022
> Answer [Truth] * MAIA Noticing | -0.36 | 0.30 | [-0.94, 0.22] | -1.23 | 0.218
> Answer [Lie] * ConditionSocial * MAIA Noticing | 0.42 | 0.10 | [ 0.23, 0.60] | 4.34 | < .001
> Answer [Truth] * ConditionSocial * MAIA Noticing | 0.07 | 0.10 | [-0.12, 0.26] | 0.73 | 0.465results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1989) | p
> --------------------------------------------------------------------------
> Polygraph | Lie | -0.68 | 0.30 | [-1.26, -0.10] | -2.28 | 0.022
> Social | Lie | -0.26 | 0.30 | [-0.84, 0.32] | -0.88 | 0.379
> Polygraph | Truth | -0.36 | 0.30 | [-0.94, 0.22] | -1.23 | 0.218
> Social | Truth | -0.29 | 0.30 | [-0.87, 0.29] | -0.99 | 0.320
> Marginal effects estimated for MAIA_Noticingp_rt_maia_noticing <- plot_effect(model, results, var = "MAIA_Noticing", outcome = "RT")Not Distracting
Confidence
model <- glmmTMB(Confidence ~ Answer / (Condition * MAIA_NotDistracting) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> -------------------------------------------------------------------------------------------------------------
> (Intercept) | -0.79 | 0.17 | [-1.12, -0.45] | -4.60 | < .001
> Answer [Truth] | 1.63 | 0.16 | [ 1.31, 1.95] | 10.02 | < .001
> Answer [Lie] * ConditionSocial | 0.32 | 0.16 | [ 0.01, 0.64] | 2.00 | 0.046
> Answer [Truth] * ConditionSocial | 2.72e-03 | 0.16 | [-0.31, 0.32] | 0.02 | 0.986
> Answer [Lie] * MAIA NotDistracting | 0.27 | 0.09 | [ 0.09, 0.45] | 2.95 | 0.003
> Answer [Truth] * MAIA NotDistracting | 0.06 | 0.09 | [-0.11, 0.24] | 0.70 | 0.483
> Answer [Lie] * ConditionSocial * MAIA NotDistracting | -0.11 | 0.09 | [-0.28, 0.06] | -1.30 | 0.194
> Answer [Truth] * ConditionSocial * MAIA NotDistracting | -0.05 | 0.09 | [-0.22, 0.11] | -0.63 | 0.527results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1980) | p
> -------------------------------------------------------------------------
> Polygraph | Lie | 0.27 | 0.09 | [ 0.09, 0.45] | 2.95 | 0.003
> Social | Lie | 0.16 | 0.09 | [-0.02, 0.34] | 1.71 | 0.087
> Polygraph | Truth | 0.06 | 0.09 | [-0.11, 0.24] | 0.70 | 0.483
> Social | Truth | 9.67e-03 | 0.09 | [-0.17, 0.19] | 0.11 | 0.915
> Marginal effects estimated for MAIA_NotDistractingp_conf_maia_notdistracting <- plot_effect(model, results, var = "MAIA_NotDistracting", outcome = "Confidence")RT
model <- glmmTMB(RT ~ Answer / (Condition * MAIA_NotDistracting) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> -------------------------------------------------------------------------------------------------------------
> (Intercept) | 4.60 | 0.49 | [ 3.64, 5.56] | 9.38 | < .001
> Answer [Truth] | -0.07 | 0.15 | [-0.37, 0.23] | -0.46 | 0.645
> Answer [Lie] * ConditionSocial | -0.49 | 0.15 | [-0.79, -0.19] | -3.23 | 0.001
> Answer [Truth] * ConditionSocial | -0.23 | 0.15 | [-0.53, 0.07] | -1.49 | 0.137
> Answer [Lie] * MAIA NotDistracting | -0.11 | 0.26 | [-0.62, 0.40] | -0.41 | 0.680
> Answer [Truth] * MAIA NotDistracting | -0.02 | 0.26 | [-0.53, 0.49] | -0.08 | 0.936
> Answer [Lie] * ConditionSocial * MAIA NotDistracting | -7.52e-03 | 0.08 | [-0.17, 0.15] | -0.09 | 0.927
> Answer [Truth] * ConditionSocial * MAIA NotDistracting | -0.17 | 0.08 | [-0.34, -0.01] | -2.10 | 0.036results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1989) | p
> -------------------------------------------------------------------------
> Polygraph | Lie | -0.11 | 0.26 | [-0.62, 0.41] | -0.41 | 0.680
> Social | Lie | -0.12 | 0.26 | [-0.63, 0.40] | -0.44 | 0.659
> Polygraph | Truth | -0.02 | 0.26 | [-0.53, 0.49] | -0.08 | 0.936
> Social | Truth | -0.19 | 0.26 | [-0.71, 0.32] | -0.74 | 0.458
> Marginal effects estimated for MAIA_NotDistractingp_rt_maia_notdistracting <- plot_effect(model, results, var = "MAIA_NotDistracting", outcome = "RT")Body Listening
Confidence
model <- glmmTMB(Confidence ~ Answer / (Condition * MAIA_BodyListening) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ------------------------------------------------------------------------------------------------------------
> (Intercept) | -1.26 | 0.20 | [-1.64, -0.87] | -6.41 | < .001
> Answer [Truth] | 2.25 | 0.19 | [ 1.89, 2.62] | 12.15 | < .001
> Answer [Lie] * ConditionSocial | 0.79 | 0.19 | [ 0.42, 1.15] | 4.22 | < .001
> Answer [Truth] * ConditionSocial | -0.12 | 0.18 | [-0.47, 0.24] | -0.63 | 0.526
> Answer [Lie] * MAIA BodyListening | 0.36 | 0.07 | [ 0.22, 0.49] | 5.13 | < .001
> Answer [Truth] * MAIA BodyListening | -0.02 | 0.07 | [-0.15, 0.12] | -0.25 | 0.802
> Answer [Lie] * ConditionSocial * MAIA BodyListening | -0.26 | 0.07 | [-0.39, -0.13] | -3.85 | < .001
> Answer [Truth] * ConditionSocial * MAIA BodyListening | 0.01 | 0.06 | [-0.12, 0.14] | 0.18 | 0.855results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1980) | p
> --------------------------------------------------------------------------
> Polygraph | Lie | 0.36 | 0.07 | [ 0.22, 0.49] | 5.13 | < .001
> Social | Lie | 0.10 | 0.07 | [-0.04, 0.24] | 1.43 | 0.153
> Polygraph | Truth | -0.02 | 0.07 | [-0.15, 0.12] | -0.25 | 0.802
> Social | Truth | -5.43e-03 | 0.07 | [-0.14, 0.13] | -0.08 | 0.937
> Marginal effects estimated for MAIA_BodyListeningp_conf_maia_bodylistening <- plot_effect(model, results, var = "MAIA_BodyListening", outcome = "Confidence")RT
model <- glmmTMB(RT ~ Answer / (Condition * MAIA_BodyListening) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params> # Fixed Effects
>
> Parameter | Coefficient | SE | 95% CI | z | p
> ------------------------------------------------------------------------------------------------------------
> (Intercept) | 5.85 | 0.53 | [ 4.81, 6.89] | 11.04 | < .001
> Answer [Truth] | -0.03 | 0.18 | [-0.38, 0.31] | -0.19 | 0.847
> Answer [Lie] * ConditionSocial | -1.36 | 0.18 | [-1.71, -1.01] | -7.69 | < .001
> Answer [Truth] * ConditionSocial | -1.19 | 0.18 | [-1.54, -0.84] | -6.73 | < .001
> Answer [Lie] * MAIA BodyListening | -0.55 | 0.19 | [-0.92, -0.19] | -2.96 | 0.003
> Answer [Truth] * MAIA BodyListening | -0.51 | 0.19 | [-0.88, -0.15] | -2.74 | 0.006
> Answer [Lie] * ConditionSocial * MAIA BodyListening | 0.33 | 0.06 | [ 0.21, 0.45] | 5.27 | < .001
> Answer [Truth] * ConditionSocial * MAIA BodyListening | 0.26 | 0.06 | [ 0.14, 0.39] | 4.19 | < .001results$marginal_effects> Estimated Marginal Effects
>
> Condition | Answer | Coefficient | SE | 95% CI | t(1989) | p
> --------------------------------------------------------------------------
> Polygraph | Lie | -0.55 | 0.19 | [-0.92, -0.19] | -2.96 | 0.003
> Social | Lie | -0.22 | 0.19 | [-0.59, 0.14] | -1.19 | 0.235
> Polygraph | Truth | -0.51 | 0.19 | [-0.88, -0.15] | -2.74 | 0.006
> Social | Truth | -0.25 | 0.19 | [-0.62, 0.12] | -1.33 | 0.183
> Marginal effects estimated for MAIA_BodyListeningp_rt_maia_bodylistening <- plot_effect(model, results, var = "MAIA_BodyListening", outcome = "RT")Correlation with LIE Scale
r <- get_correlation(var = "MAIA_")
r$plot +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
Summary
p_conf_maia_total /
p_conf_maia_noticing /
p_conf_maia_notdistracting /
p_conf_maia_bodylistening +
patchwork::plot_annotation(title = "Interoception (MAIA)", theme = theme(plot.title = element_text(hjust = 0.5)))
p_rt_maia_total /
p_rt_maia_noticing /
p_rt_maia_notdistracting /
p_rt_maia_bodylistening +
patchwork::plot_annotation(title = "Interoception (MAIA)", theme = theme(plot.title = element_text(hjust = 0.5)))
Full Code
The full script of executive code contained in this document is reproduced here.
# Set up the environment (or use local alternative `source("utils/config.R")`)
source("https://raw.githubusercontent.com/RealityBending/TemplateResults/main/utils/config.R")
options(dplyr.summarise.inform = FALSE)
knitr::opts_chunk$set(echo = TRUE)
library(ggplot2)
theme_set(see::theme_modern())
# This chunk is a bit complex so don't worry about it: it's made to add badges to the HTML versions
# NOTE: You have to replace the links accordingly to have working "buttons" on the HTML versions
if (!knitr::is_latex_output() && knitr::is_html_output()) {
cat("
[](https://github.com/RealityBending/TemplateResults)
[](https://realitybending.github.io/TemplateResults/)
[](https://github.com/RealityBending/TemplateResults/raw/main/word_and_pdf/SupplementaryMaterials.docx)
[](https://github.com/RealityBending/TemplateResults/blob/main/word_and_pdf/SupplementaryMaterials.pdf)")
}
library(tidyverse)
library(patchwork)
library(glmmTMB)
library(report)
library(parameters)
library(correlation)
library(modelbased)
library(performance)
library(see)
summary(report::report(sessionInfo()))
# setwd("C:/Users/user/Desktop/Sputnik/2019-23/DeceptionInteroTom")
df <- read.csv("data/data_combined.csv") %>%
mutate(ID = as.factor(paste0("S", ID)),
condition = as.factor(condition),
item = as.factor(item),
style = as.factor(style),
instruction = as.factor(instruction)) |>
#TODO: This renaming should be done at the preprocessing stage
rename("Participant" = "ID",
"Condition" = "condition",
"Item" = "item",
"Phrasing" = "style",
"Answer" = "instruction",
"YONI_Total" = "yoni_total",
"YONI_Affective" = "yoni_affective",
"YONI_Cognitive" = "yoni_cognitive",
"YONI_Physical" = "yoni_physical",
"BES_Total" = "BES_total",
"BES_Cognitive" = "BES_cognitive",
"BES_Affective" = "BES_affective",
"HCT_Confidence" = "HCT_confidence",
"HCT_Accuracy" = "HCT_accuracy",
"HCT_Awareness" = "HCT_awareness",
"MAIA_Total" = "MAIA_total",
"MAIA_AttentionRegulation" = "MAIA_attention_regulation",
"MAIA_BodyListening" = "MAIA_body_listening",
"MAIA_EmotionalAwareness" = "MAIA_emotional_awareness",
"MAIA_NotDistracting" = "MAIA_not_distracting",
"MAIA_NotWorrying" = "MAIA_not_worrying",
"MAIA_Noticing" = "MAIA_noticing",
"MAIA_SelfRegulation" = "MAIA_self_regulation",
"MAIA_Trusting" = "MAIA_trusting",
"LIE_Ability" = "lie_ability",
"LIE_Frequency" = "lie_frequency",
"LIE_Negativity" = "lie_negativity",
"LIE_Contextuality" = "lie_contextuality",
"Confidence" = "DT_confidence",
"RT" = "DT_RT") |>
mutate(Answer = fct_recode(Answer, Lie = "LIE", Truth = "TRUTH")) |>
select(-HCT_guess, -HCT_noguess, -HCT_onebreath)
cat(paste("The data consists of",
report::report_participants(df,
participants = "Participant",
sex = "Gender",
age = "Age")))
report::cite_packages(sessionInfo())
df %>%
group_by(Participant) %>%
select(starts_with("YONI_")) |>
summarise_all(mean, na.rm=TRUE) |>
tidyr::pivot_longer(-Participant, values_to = "Scores") |>
mutate(name = paste0(str_replace(name, "_", " ("), ")")) |>
ggplot(aes(x = Scores, fill = name)) +
geom_density() +
scale_fill_manual(values = c("YONI (Affective)" = "Purple",
"YONI (Cognitive)" = "Blue",
"YONI (Physical)" = "Green",
"YONI (Total)"= "DarkBlue"),
guide = "none") +
facet_wrap(~name, scales = "free")
df %>%
group_by(Participant) %>%
select(starts_with("BES_")) |>
summarise_all(mean, na.rm=TRUE) |>
tidyr::pivot_longer(-Participant, values_to = "Scores") |>
mutate(name = paste0(str_replace(name, "_", " ("), ")")) |>
ggplot(aes(x = Scores, fill = name)) +
geom_density() +
scale_fill_manual(values = c("BES (Affective)" = "Purple",
"BES (Cognitive)" = "Blue",
"BES (Total)"= "DarkBlue"),
guide = "none") +
facet_wrap(~name, scales = "free")
df %>%
group_by(Participant) %>%
select(starts_with("HCT_")) |>
summarise_all(mean, na.rm=TRUE) |>
tidyr::pivot_longer(-Participant, values_to = "Scores") |>
mutate(name = paste0(str_replace(name, "_", " ("), ")")) |>
ggplot(aes(x = Scores, fill = name)) +
geom_density() +
scale_fill_manual(values = c("HCT (Accuracy)" = "Red",
"HCT (Awareness)" = "Orange",
"HCT (Confidence)"= "DarkOrange"),
guide = "none") +
facet_wrap(~name, scales = "free")
df %>%
group_by(Participant) %>%
select(starts_with("MAIA_")) |>
summarise_all(mean, na.rm=TRUE) |>
tidyr::pivot_longer(-Participant, values_to = "Scores") |>
mutate(name = paste0(str_replace(name, "_", " ("), ")")) |>
ggplot(aes(x = Scores, fill = name)) +
geom_density() +
scale_fill_brewer(palette = "Reds", guide = "none") +
facet_wrap(~name, scales = "free")
df %>%
group_by(Participant) %>%
select(starts_with("LIE_")) |>
summarise_all(mean, na.rm=TRUE) |>
tidyr::pivot_longer(-Participant, values_to = "Scores") |>
mutate(name = paste0(str_replace(name, "_", " ("), ")")) |>
ggplot(aes(x = Scores, fill = name)) +
geom_density() +
scale_fill_manual(values = c("LIE (Ability)" = "#2196F3",
"LIE (Frequency)" = "#4CAF50",
"LIE (Contextuality)"= "#FF9800",
"LIE (Negativity)"= "#E91E63"),
guide = "none") +
facet_wrap(~name, scales = "free")
df |>
group_by(Participant, Answer) |>
summarise(Confidence = paste(insight::format_value(mean(Confidence, na.rm = TRUE)),
" +- ",
insight::format_value(sd(Confidence, na.rm = TRUE))),
RT = paste(insight::format_value(mean(RT, na.rm = TRUE)),
" +- ",
insight::format_value(sd(RT, na.rm = TRUE)))) |>
arrange(Participant) |>
knitr::kable()
df <- df |>
dplyr::filter(Participant != "S9", # Extreme answers
!Participant %in% c("S3", "S15", "S19", "S23")) # No data
p1 <- df |>
dplyr::filter(!Participant %in% c("S29")) |>
ggplot(aes(x = Confidence, fill = Participant)) +
geom_density(alpha = 0.1) +
see::scale_fill_material_d(palette = "rainbow", guide = "none") +
see::theme_modern() +
scale_x_continuous(labels = scales::percent, expand=expansion(c(0, .05))) +
scale_y_continuous(expand=expansion(c(0, .05))) +
facet_wrap(~Answer)
p2 <- df |>
dplyr::filter(!Participant %in% c("S29")) |>
ggplot(aes(x = RT, fill = Participant)) +
geom_density(alpha = 0.1) +
see::scale_fill_material_d(palette = "rainbow", guide = "none") +
scale_x_continuous(expand=expansion(c(0, .05))) +
scale_y_continuous(expand=expansion(c(0, .05))) +
facet_wrap(~Answer)
p1 / p2
dfsub <- df |>
select(Participant,
starts_with("YONI_"),
starts_with("BES_"),
starts_with("HCT_"),
starts_with("MAIA_"),
starts_with("LIE_")) |>
group_by(Participant) |>
summarise_all(mean)
r <- correlation(select(dfsub, starts_with("YONI_")),
select(dfsub, starts_with("BES_")),
p_adjust = "none")
summary(r) |>
plot()
r <- correlation(select(dfsub, starts_with("MAIA_")),
select(dfsub, starts_with("HCT_")),
p_adjust = "none")
summary(r) |>
plot()
r <- correlation(select(dfsub, starts_with(c("MAIA_", "HCT_"))),
select(dfsub, starts_with(c("YONI_", "BES_"))),
p_adjust = "none")
summary(r) |>
plot()
model <- glmmTMB(RT ~ Answer * Phrasing + (1|Participant) + (1|Item), data = df)
parameters::parameters(model, effects = "fixed")
estimate_means(model, at = c("Answer", "Phrasing")) |>
plot(show_data = "none")
model <- glmmTMB(Confidence ~ Answer * Phrasing + (1|Participant) + (1|Item), data = df)
parameters::parameters(model)
estimate_means(model, at = c("Answer", "Phrasing")) |>
plot(show_data = "none")
# Adjustments for beta models
df$Confidence[df$Confidence == 1] <- 0.99999
df$Confidence[df$Confidence == 0] <- 0.00001
model <- glmmTMB(Confidence ~ RT * Answer + Phrasing + (1|Participant) + (1|Item),
data = df, family = beta_family())
parameters::parameters(model, effects = "fixed")
estimate_relation(model, at = c("RT", "Answer")) |>
plot(length = 50, point = list(alpha = 0.3, size = 3.5))
model <- glmmTMB(Confidence ~ Answer * Condition + (1|Participant) + (1|Item),
data = df, family = beta_family())
parameters::parameters(model, effects = "fixed")
estimate_means(model, at = c("Condition", "Answer")) |>
plot(show_data = "none")
model <- glmmTMB(RT ~ Answer * Condition + (1|Participant) + (1|Item),
data = df)
parameters::parameters(model, effects = "fixed")
estimate_means(model, at = c("Condition", "Answer")) |>
plot(show_data = "none")
model <- glmmTMB(Confidence ~ Answer / (Condition * YONI_Total) + (1|Participant) + (1|Item),
data = df, family = beta_family())
get_parameters <- function(model) {
# Parameters
params <- parameters::parameters(model, effects = "fixed")
# Marginal effects
at <- c("Answer", "Condition")
trend <- insight::find_predictors(model)$conditional
trend <- trend[!trend %in% at]
marg <- modelbased::estimate_slopes(model, trend = trend, at = at)
# Output
list(params = params, marginal_effects = marg)
}
results <- get_parameters(model)
results$params
results$marginal_effects
plot_effect <- function(model, results, var = "YONI_Total", outcome = "Confidence") {
data <- df |>
group_by(Participant, Answer, Condition) |>
summarise({{var}} := mean(.data[[var]], na.rm = TRUE),
SD = sd(.data[[outcome]], na.rm = TRUE),
{{outcome}} := mean(.data[[outcome]], na.rm = TRUE),
CI_low = .data[[outcome]] - SD / 2,
CI_high = .data[[outcome]] + SD / 2)
link_data <- estimate_relation(model, at = c("Condition", var, "Answer"), length = 30)
range_x <- diff(range(data[[var]]))
annot <- results$marginal_effects
annot$Label <- insight::format_p(annot$p, stars_only = TRUE)
annot$x <- mean(range(data[[var]]))
annot$x <- ifelse(annot$Condition == "Polygraph",
annot$x - 0.05 * range_x,
annot$x + 0.05 * range_x)
annot$y <- max(data[[outcome]]) - 0.01 * diff(range(data[[outcome]]))
p <- ggplot(link_data, aes_string(x = var, y = "Predicted")) +
geom_pointrange(data = data,
aes_string(y = outcome,
color = "Condition",
ymin = "CI_low",
ymax = "CI_high"),
position = position_dodge(width = 0.02 * range_x)) +
geom_ribbon(aes(ymin = CI_low, ymax = CI_high, fill = Condition), alpha = 0.33) +
geom_line(aes(color = Condition)) +
geom_text(data=annot, aes(x = x, y = y, label = Label, color = Condition),
size = 8, show.legend = FALSE) +
labs(y = ifelse(outcome == "RT", "Reaction Time (s)", "Confidence"),
x = paste0(stringr::str_replace(var, "_", " ("), ")")) +
scale_color_manual(values = c("Polygraph" = "#FF5722", "Social" = "#2196F3")) +
scale_fill_manual(values = c("Polygraph" = "#FF5722", "Social" = "#2196F3")) +
facet_wrap(~Answer)
# Narrow y range
if(outcome == "Confidence") {
p <- p +
coord_cartesian(ylim = c(0, 1)) +
scale_y_continuous(expand=expansion(c(0, 0)))
}
p
}
p_conf_yoni_total <- plot_effect(model, results, var = "YONI_Total", outcome = "Confidence")
# p_conf_yoni_total
model <- glmmTMB(RT ~ Answer / (Condition * YONI_Total) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params
results$marginal_effects
p_rt_yoni_total <- plot_effect(model, results, var = "YONI_Total", outcome = "RT")
# p_rt_yoni_total
model <- glmmTMB(Confidence ~ Answer / (Condition * YONI_Cognitive) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params
results$marginal_effects
p_conf_yoni_cognitive <- plot_effect(model, results, var = "YONI_Cognitive", outcome = "Confidence")
# p_conf_yoni_cognitive
model <- glmmTMB(RT ~ Answer / (Condition * YONI_Cognitive) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params
results$marginal_effects
p_rt_yoni_cognitive <- plot_effect(model, results, var = "YONI_Cognitive", outcome = "RT")
# p_rt_yoni_cognitive
model <- glmmTMB(Confidence ~ Answer / (Condition * YONI_Affective) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params
results$marginal_effects
p_conf_yoni_affective <- plot_effect(model, results, var = "YONI_Affective", outcome = "Confidence")
# p_conf_yoni_affective
model <- glmmTMB(RT ~ Answer / (Condition * YONI_Affective) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params
results$marginal_effects
p_rt_yoni_affective <- plot_effect(model, results, var = "YONI_Affective", outcome = "RT")
# p_rt_yoni_affective
model <- glmmTMB(Confidence ~ Answer / (Condition * YONI_Physical) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params
results$marginal_effects
p_conf_yoni_physical <- plot_effect(model, results, var = "YONI_Physical", outcome = "Confidence")
# p_conf_yoni_physical
model <- glmmTMB(RT ~ Answer / (Condition * YONI_Physical) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params
results$marginal_effects
p_rt_yoni_physical <- plot_effect(model, results, var = "YONI_Physical", outcome = "RT")
# p_rt_yoni_physical
get_correlation <- function(var = "YONI_", var2 = "LIE_") {
r <- correlation(select(dfsub, starts_with(var2)),
select(dfsub, starts_with(var)),
p_adjust = "none") |>
mutate(Parameter1 = paste0(str_replace(Parameter1, "_", " ("), ")"),
Parameter2 = paste0(str_replace(Parameter2, "_", " ("), ")"))
p <- summary(r) |>
plot() +
theme_minimal()
list(r = r, plot = p)
}
r <- get_correlation(var = "YONI_")
r$plot
p_conf_yoni_total /
p_conf_yoni_cognitive /
p_conf_yoni_affective /
p_conf_yoni_physical +
patchwork::plot_annotation(title = "Theory of Mind", theme = theme(plot.title = element_text(hjust = 0.5)))
p_rt_yoni_total /
p_rt_yoni_cognitive /
p_rt_yoni_affective /
p_rt_yoni_physical +
patchwork::plot_annotation(title = "Theory of Mind", theme = theme(plot.title = element_text(hjust = 0.5)))
model <- glmmTMB(Confidence ~ Answer / (Condition * BES_Total) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params
results$marginal_effects
p_conf_bes_total <- plot_effect(model, results, var = "BES_Total", outcome = "Confidence")
# p_conf_bes_total
model <- glmmTMB(RT ~ Answer / (Condition * BES_Total) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params
results$marginal_effects
p_rt_bes_total <- plot_effect(model, results, var = "BES_Total", outcome = "RT")
# p_rt_bes_total
model <- glmmTMB(Confidence ~ Answer / (Condition * BES_Cognitive) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params
results$marginal_effects
p_conf_bes_cognitive <- plot_effect(model, results, var = "BES_Cognitive", outcome = "Confidence")
# p_conf_bes_cognitive
model <- glmmTMB(RT ~ Answer / (Condition * BES_Cognitive) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params
results$marginal_effects
p_rt_bes_cognitive <- plot_effect(model, results, var = "BES_Cognitive", outcome = "RT")
# p_rt_bes_cognitive
model <- glmmTMB(Confidence ~ Answer / (Condition * BES_Affective) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params
results$marginal_effects
p_conf_bes_affective <- plot_effect(model, results, var = "BES_Affective", outcome = "Confidence")
# p_conf_bes_affective
model <- glmmTMB(RT ~ Answer / (Condition * BES_Affective) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params
results$marginal_effects
p_rt_bes_affective <- plot_effect(model, results, var = "BES_Affective", outcome = "RT")
# p_rt_bes_affective
r <- get_correlation(var = "BES_")
r$plot
p_conf_bes_total /
p_conf_bes_cognitive /
p_conf_bes_affective +
patchwork::plot_annotation(title = "Empathy", theme = theme(plot.title = element_text(hjust = 0.5)))
p_rt_bes_total /
p_rt_bes_cognitive /
p_rt_bes_affective +
patchwork::plot_annotation(title = "Empathy", theme = theme(plot.title = element_text(hjust = 0.5)))
model <- glmmTMB(Confidence ~ Answer / (Condition * HCT_Accuracy) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params
results$marginal_effects
p_conf_hct_accuracy <- plot_effect(model, results, var = "HCT_Accuracy", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * HCT_Accuracy) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params
results$marginal_effects
p_rt_hct_accuracy <- plot_effect(model, results, var = "HCT_Accuracy", outcome = "RT")
model <- glmmTMB(Confidence ~ Answer / (Condition * HCT_Confidence) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params
results$marginal_effects
p_conf_hct_confidence <- plot_effect(model, results, var = "HCT_Confidence", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * HCT_Confidence) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params
results$marginal_effects
p_rt_hct_confidence <- plot_effect(model, results, var = "HCT_Confidence", outcome = "RT")
model <- glmmTMB(Confidence ~ Answer / (Condition * HCT_Awareness) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params
results$marginal_effects
p_conf_hct_awareness <- plot_effect(model, results, var = "HCT_Awareness", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * HCT_Awareness) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params
results$marginal_effects
p_rt_hct_awareness <- plot_effect(model, results, var = "HCT_Awareness", outcome = "RT")
r <- get_correlation(var = "HCT_")
r$plot
# plot(correlation::cor_test(dfsub, "HCT_Accuracy", "LIE_Ability"))
p_conf_hct_accuracy /
p_conf_hct_confidence /
p_conf_hct_awareness +
patchwork::plot_annotation(title = "Interoception", theme = theme(plot.title = element_text(hjust = 0.5)))
p_rt_hct_accuracy /
p_rt_hct_confidence /
p_rt_hct_awareness +
patchwork::plot_annotation(title = "Interoception", theme = theme(plot.title = element_text(hjust = 0.5)))
model <- glmmTMB(Confidence ~ Answer / (Condition * MAIA_Total) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params
results$marginal_effects
p_conf_maia_total <- plot_effect(model, results, var = "MAIA_Total", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * MAIA_Total) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params
results$marginal_effects
p_rt_maia_total <- plot_effect(model, results, var = "MAIA_Total", outcome = "RT")
model <- glmmTMB(Confidence ~ Answer / (Condition * MAIA_Noticing) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params
results$marginal_effects
p_conf_maia_noticing <- plot_effect(model, results, var = "MAIA_Noticing", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * MAIA_Noticing) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params
results$marginal_effects
p_rt_maia_noticing <- plot_effect(model, results, var = "MAIA_Noticing", outcome = "RT")
model <- glmmTMB(Confidence ~ Answer / (Condition * MAIA_NotDistracting) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params
results$marginal_effects
p_conf_maia_notdistracting <- plot_effect(model, results, var = "MAIA_NotDistracting", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * MAIA_NotDistracting) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params
results$marginal_effects
p_rt_maia_notdistracting <- plot_effect(model, results, var = "MAIA_NotDistracting", outcome = "RT")
model <- glmmTMB(Confidence ~ Answer / (Condition * MAIA_BodyListening) + (1|Participant) + (1|Item),
data = df, family = beta_family())
results <- get_parameters(model)
results$params
results$marginal_effects
p_conf_maia_bodylistening <- plot_effect(model, results, var = "MAIA_BodyListening", outcome = "Confidence")
model <- glmmTMB(RT ~ Answer / (Condition * MAIA_BodyListening) + (1|Participant) + (1|Item),
data = df)
results <- get_parameters(model)
results$params
results$marginal_effects
p_rt_maia_bodylistening <- plot_effect(model, results, var = "MAIA_BodyListening", outcome = "RT")
r <- get_correlation(var = "MAIA_")
r$plot +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
p_conf_maia_total /
p_conf_maia_noticing /
p_conf_maia_notdistracting /
p_conf_maia_bodylistening +
patchwork::plot_annotation(title = "Interoception (MAIA)", theme = theme(plot.title = element_text(hjust = 0.5)))
p_rt_maia_total /
p_rt_maia_noticing /
p_rt_maia_notdistracting /
p_rt_maia_bodylistening +
patchwork::plot_annotation(title = "Interoception (MAIA)", theme = theme(plot.title = element_text(hjust = 0.5)))Package References
report::cite_packages(sessionInfo())- Ben-Shachar M, Lüdecke D, Makowski D (2020). effectsize: Estimation of Effect Size Indices and Standardized Parameters. Journal of Open Source Software, 5(56), 2815. doi: 10.21105/joss.02815
- H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
- Hadley Wickham (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.4.0. https://CRAN.R-project.org/package=stringr
- Hadley Wickham (2021). forcats: Tools for Working with Categorical Variables (Factors). R package version 0.5.1. https://CRAN.R-project.org/package=forcats
- Hadley Wickham (2021). tidyr: Tidy Messy Data. R package version 1.1.4. https://CRAN.R-project.org/package=tidyr
- Hadley Wickham and Jim Hester (2021). readr: Read Rectangular Text Data. R package version 2.0.2. https://CRAN.R-project.org/package=readr
- Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.7. https://CRAN.R-project.org/package=dplyr
- Kirill Müller and Hadley Wickham (2021). tibble: Simple Data Frames. R package version 3.1.4. https://CRAN.R-project.org/package=tibble
- Lionel Henry and Hadley Wickham (2020). purrr: Functional Programming Tools. R package version 0.3.4. https://CRAN.R-project.org/package=purrr
- Lüdecke D, Ben-Shachar M, Patil I, Makowski D (2020). “Extracting,Computing and Exploring the Parameters of Statistical Models using R.”Journal of Open Source Software, 5(53), 2445. doi:10.21105/joss.02445 (URL: https://doi.org/10.21105/joss.02445).
- Lüdecke D, Waggoner P, Makowski D (2019). “insight: A Unified Interfaceto Access Information from Model Objects in R.” Journal of Open SourceSoftware, 4(38), 1412. doi: 10.21105/joss.01412 (URL:https://doi.org/10.21105/joss.01412).
- Lüdecke et al., (2021). performance: An R Package for Assessment, Comparison and Testing of Statistical Models. Journal of Open Source Software, 6(60), 3139. https://doi.org/10.21105/joss.03139
- Lüdecke et al., (2021). see: An R Package for Visualizing Statistical Models. Journal of Open Source Software, 6(64), 3393. https://doi.org/10.21105/joss.03393
- Makowski, D., Ben-Shachar, M. S. & Lüdecke, D. (2020). The {easystats} collection of R packages. GitHub.
- Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2019). Methods and Algorithms for Correlation Analysis in R. Journal of Open Source Software, 5(51), 2306. doi:10.21105/joss.02306
- Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2020). Estimation of Model-Based Predictions, Contrasts and Means. CRAN.
- Makowski, D., Ben-Shachar, M., & Lüdecke, D. (2019). bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework. Journal of Open Source Software, 4(40), 1541. doi:10.21105/joss.01541
- Makowski, D., Ben-Shachar, M.S., Patil, I. & Lüdecke, D. (2020). Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption. CRAN. Available from https://github.com/easystats/report. doi: .
- Makowski, Lüdecke, Patil, Ben-Shachar, & Wiernik (2021). datawizard: Easy Data Wrangling. CRAN. Available from https://easystats.github.io/datawizard/
- Mollie E. Brooks, Kasper Kristensen, Koen J. van Benthem, Arni Magnusson, Casper W. Berg, Anders Nielsen, Hans J. Skaug, Martin Maechler and Benjamin M. Bolker (2017). glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. The R Journal, 9(2), 378-400.
- R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
- Thomas Lin Pedersen (2020). patchwork: The Composer of Plots. R package version 1.1.1. https://CRAN.R-project.org/package=patchwork
- Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686
References
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